Controls Briefs for AI Systems
Before AI ships, define what it can refuse, when it should escalate, and who owns the outcome.
Controls before features
The default sequence for AI deployment is: identify a use case, build a model, test accuracy, ship to production, then — maybe — define controls. This sequence produces systems that work in demos and fail in operations. Controls are not constraints on innovation. They are the infrastructure that makes innovation safe enough to deploy. A controls brief is a one-page document that answers five questions before the first model is trained: What decisions will this system influence? What is the maximum acceptable error rate for each decision type? How will errors be detected and by whom? What is the escalation path when the system produces an output that an operator questions? Who is accountable for the business outcome? These questions seem obvious. In practice, fewer than one in five AI deployments we review can answer all of them. Teams skip them because they feel bureaucratic, because the answers seem self-evident, or because the timeline does not allow for the conversation. The result is AI systems that ship fast and create risk faster. When an AI system produces an incorrect recommendation that a customer acts on, the first question from legal will be: 'What controls were in place?' If the answer is 'we were going to define those after launch,' the conversation is already over.
Escalation is not optional
Every AI system needs an escalation path. Not a theoretical path documented in a governance policy — an operational path that triggers automatically when specific conditions are met. Escalation conditions should be defined for at least four scenarios: when model confidence falls below a threshold, when the input falls outside the training distribution, when an operator overrides the model's recommendation, and when the model's output contradicts a business rule. For each scenario, the controls brief specifies who receives the alert, what information they need to make a decision, and how long they have to respond before the system defaults to a safe fallback. Most AI systems in production today have no escalation path. When the model is uncertain, it guesses. When an operator overrides it, no one notices. When the output contradicts policy, the contradiction is invisible until audit. This is not a technology gap — escalation logic is straightforward to implement. It is a design gap. Teams do not build escalation paths because no one required them to. The controls brief makes escalation a requirement, not an afterthought.
Auditability is a requirement
If you cannot explain why an AI system produced a specific output, you cannot defend it to a regulator, a customer, or a court. Auditability is not a nice-to-have feature for mature deployments. It is a day-one requirement for any AI system that influences decisions affecting people, money, or compliance. An auditable AI system logs four things for every decision: the input the model received, the output the model produced, the confidence level associated with that output, and whether a human reviewed or overrode it. These logs must be immutable, timestamped, and retained according to the organization's data retention policy. Beyond logging, auditability requires traceability. When an outcome is questioned — 'why did the system recommend X?' — the team must be able to reconstruct the chain of events: which model version was running, what training data it was built on, what configuration parameters were active, and whether any upstream data sources had changed since the model was last validated. This level of traceability sounds expensive. It is less expensive than the alternative: an AI system that produces outcomes no one can explain, operating in a regulatory environment that increasingly requires explanations.
The brief is one page, not one hundred
A controls brief is deliberately concise. It fits on one page because it is meant to be read, discussed, and updated — not filed and forgotten. One page forces clarity. It prevents the team from hiding uncertainty behind volume. If the answer to 'who owns the outcome?' requires a paragraph of caveats, the ownership is not clear enough. The brief should be written by the technical lead and the business owner together. The technical lead brings understanding of what the model can and cannot do. The business owner brings understanding of what outcomes matter and what level of risk is acceptable. Neither perspective alone is sufficient. After the brief is written, it is reviewed by operations, compliance, and security. Each reviewer has one question: 'Is there anything in this brief that I would not be comfortable defending in a post-incident review?' If the answer is yes, the brief is revised before development begins. The controls brief is not bureaucracy. It is the shortest path to deploying AI with confidence. Teams that write one spend a few hours and gain organizational trust. Teams that skip it spend weeks cleaning up incidents and rebuilding credibility.